Understanding Azure Data Factory
Improve your analytics and data platform to solve major challenges, including operationalizing big data and advanced analytics workloads on Azure. You will learn how to monitor complex pipelines, set alerts, and extend your organization's custom monitoring requirements. This book starts with an overview of the Azure Data Factory as a hybrid ETL/ELT orchestration service on Azure. The book then dives into data movement and the connectivity capability of Azure Data Factory. You will learn about the support for hybrid data integration from disparate sources such as on-premise, cloud, or from SaaS applications. Detailed guidance is provided on how to transform data and on control flow. Demonstration of operationalizing the pipelines and ETL with SSIS is included. You will know how to leverage Azure Data Factory to run existing SSIS packages. As you advance through the book, you will wrap up by learning how to create a single pane for end-to-end monitoring, which is a key skill in buildi ...
Improve your analytics and data platform to solve major challenges, including operationalizing big data and advanced analytics workloads on Azure. You will learn how to monitor complex pipelines, set alerts, and extend your organization's custom monitoring requirements. This book starts with an overview of the Azure Data Factory as a hybrid ETL/ELT orchestration service on Azure. The book then dives into data movement and the connectivity capability of Azure Data Factory. You will learn about the support for hybrid data integration from disparate sources such as on-premise, cloud, or from SaaS applications. Detailed guidance is provided on how to transform data and on control flow. Demonstration of operationalizing the pipelines and ETL with SSIS is included. You will know how to leverage Azure Data Factory to run existing SSIS packages. As you advance through the book, you will wrap up by learning how to create a single pane for end-to-end monitoring, which is a key skill in buildi ...
Oracle High Availability, Disaster Recovery, and Cloud Services
Work with Oracle database's high-availability and disaster-management technologies. This book covers all the Oracle high-availability technologies in one place and also discusses how you configure them in engineered systems and cloud services. You will see that when you say your database is healthy, it is not limited to whether the database is performing well on day-to-day operations; rather it should also be robust and free from disasters. As a result, your database will be capable of handling unforeseen incidents and recovering from disaster with very minimal or zero downtime. Oracle High Availability, Disaster Recovery, and Cloud Services explores all the high-availability features of Oracle database, how to configure them, and best practices. After you have read this book you will have mastered database high-availability concepts such as RAC, Data Guard, OEM 13c, and engineered systems (Oracle Exadata x6/x7 and Oracle Database Appliance). Master the best practices and feat ...
Work with Oracle database's high-availability and disaster-management technologies. This book covers all the Oracle high-availability technologies in one place and also discusses how you configure them in engineered systems and cloud services. You will see that when you say your database is healthy, it is not limited to whether the database is performing well on day-to-day operations; rather it should also be robust and free from disasters. As a result, your database will be capable of handling unforeseen incidents and recovering from disaster with very minimal or zero downtime. Oracle High Availability, Disaster Recovery, and Cloud Services explores all the high-availability features of Oracle database, how to configure them, and best practices. After you have read this book you will have mastered database high-availability concepts such as RAC, Data Guard, OEM 13c, and engineered systems (Oracle Exadata x6/x7 and Oracle Database Appliance). Master the best practices and feat ...
The Self-Service Data Roadmap
Data-driven insights are a key competitive advantage for any industry today, but deriving insights from raw data can still take days or weeks. Most organizations can't scale data science teams fast enough to keep up with the growing amounts of data to transform. What's the answer? Self-service data. With this practical book, data engineers, data scientists, and team managers will learn how to build a self-service data science platform that helps anyone in your organization extract insights from data. Sandeep Uttamchandani provides a scorecard to track and address bottlenecks that slow down time to insight across data discovery, transformation, processing, and production. This book bridges the gap between data scientists bottlenecked by engineering realities and data engineers unclear about ways to make self-service work. ...
Data-driven insights are a key competitive advantage for any industry today, but deriving insights from raw data can still take days or weeks. Most organizations can't scale data science teams fast enough to keep up with the growing amounts of data to transform. What's the answer? Self-service data. With this practical book, data engineers, data scientists, and team managers will learn how to build a self-service data science platform that helps anyone in your organization extract insights from data. Sandeep Uttamchandani provides a scorecard to track and address bottlenecks that slow down time to insight across data discovery, transformation, processing, and production. This book bridges the gap between data scientists bottlenecked by engineering realities and data engineers unclear about ways to make self-service work. ...
Introduction to Data Science
The demand for skilled data science practitioners in industry, academia, and government is rapidly growing. This book introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression and machine learning. It also helps you develop skills such as R programming, data wrangling with dplyr, data visualization with ggplot2, algorithm building with caret, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation with knitr and R markdown. The book is divided into six parts: R, Data Visualization, Data Wrangling, Probability, Inference and Regression with R, Machine Learning, and Productivity Tools. Each part has several chapters meant to be presented as one lecture. The book includes dozens of exercises distributed across most chapters. ...
The demand for skilled data science practitioners in industry, academia, and government is rapidly growing. This book introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression and machine learning. It also helps you develop skills such as R programming, data wrangling with dplyr, data visualization with ggplot2, algorithm building with caret, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation with knitr and R markdown. The book is divided into six parts: R, Data Visualization, Data Wrangling, Probability, Inference and Regression with R, Machine Learning, and Productivity Tools. Each part has several chapters meant to be presented as one lecture. The book includes dozens of exercises distributed across most chapters. ...
SQL Server Data Automation Through Frameworks
Learn to automate SQL Server operations using frameworks built from metadata-driven stored procedures and SQL Server Integration Services (SSIS). Bring all the power of Transact-SQL (T-SQL) and Microsoft .NET to bear on your repetitive data, data integration, and ETL processes. Do this for no added cost over what you've already spent on licensing SQL Server. The tools and methods from this book may be applied to on-premises and Azure SQL Server instances. The SSIS framework from this book works in Azure Data Factory (ADF) and provides DevOps personnel the ability to execute child packages outside a project - functionality not natively available in SSIS. Frameworks not only reduce the time required to deliver enterprise functionality, but can also accelerate troubleshooting and problem resolution. You'll learn in this book how frameworks also improve code quality by using metadata to drive processes. Much of the work performed by data professionals can be classified as "drudge work" ...
Learn to automate SQL Server operations using frameworks built from metadata-driven stored procedures and SQL Server Integration Services (SSIS). Bring all the power of Transact-SQL (T-SQL) and Microsoft .NET to bear on your repetitive data, data integration, and ETL processes. Do this for no added cost over what you've already spent on licensing SQL Server. The tools and methods from this book may be applied to on-premises and Azure SQL Server instances. The SSIS framework from this book works in Azure Data Factory (ADF) and provides DevOps personnel the ability to execute child packages outside a project - functionality not natively available in SSIS. Frameworks not only reduce the time required to deliver enterprise functionality, but can also accelerate troubleshooting and problem resolution. You'll learn in this book how frameworks also improve code quality by using metadata to drive processes. Much of the work performed by data professionals can be classified as "drudge work" ...
Data Pipelines Pocket Reference
Data pipelines are the foundation for success in data analytics. Moving data from numerous diverse sources and transforming it to provide context is the difference between having data and actually gaining value from it. This pocket reference defines data pipelines and explains how they work in today's modern data stack. You'll learn common considerations and key decision points when implementing pipelines, such as batch versus streaming data ingestion and build versus buy. This book addresses the most common decisions made by data professionals and discusses foundational concepts that apply to open source frameworks, commercial products, and homegrown solutions. You'll learn: What a data pipeline is and how it works; How data is moved and processed on modern data infrastructure, including cloud platforms; Common tools and products used by data engineers to build pipelines; How pipelines support analytics and reporting needs; Considerations for pipeline maintenance, testing, and a ...
Data pipelines are the foundation for success in data analytics. Moving data from numerous diverse sources and transforming it to provide context is the difference between having data and actually gaining value from it. This pocket reference defines data pipelines and explains how they work in today's modern data stack. You'll learn common considerations and key decision points when implementing pipelines, such as batch versus streaming data ingestion and build versus buy. This book addresses the most common decisions made by data professionals and discusses foundational concepts that apply to open source frameworks, commercial products, and homegrown solutions. You'll learn: What a data pipeline is and how it works; How data is moved and processed on modern data infrastructure, including cloud platforms; Common tools and products used by data engineers to build pipelines; How pipelines support analytics and reporting needs; Considerations for pipeline maintenance, testing, and a ...
Data Science Revealed
Get insight into data science techniques such as data engineering and visualization, statistical modeling, machine learning, and deep learning. This book teaches you how to select variables, optimize hyper parameters, develop pipelines, and train, test, and validate machine and deep learning models. Each chapter includes a set of examples allowing you to understand the concepts, assumptions, and procedures behind each model. The book covers parametric methods or linear models that combat under- or over-fitting using techniques such as Lasso and Ridge. It includes complex regression analysis with time series smoothing, decomposition, and forecasting. It takes a fresh look at non-parametric models for binary classification (logistic regression analysis) and ensemble methods such as decision trees, support vector machines, and naive Bayes. It covers the most popular non-parametric method for time-event data (the Kaplan-Meier estimator). It also covers ways of solving classification pro ...
Get insight into data science techniques such as data engineering and visualization, statistical modeling, machine learning, and deep learning. This book teaches you how to select variables, optimize hyper parameters, develop pipelines, and train, test, and validate machine and deep learning models. Each chapter includes a set of examples allowing you to understand the concepts, assumptions, and procedures behind each model. The book covers parametric methods or linear models that combat under- or over-fitting using techniques such as Lasso and Ridge. It includes complex regression analysis with time series smoothing, decomposition, and forecasting. It takes a fresh look at non-parametric models for binary classification (logistic regression analysis) and ensemble methods such as decision trees, support vector machines, and naive Bayes. It covers the most popular non-parametric method for time-event data (the Kaplan-Meier estimator). It also covers ways of solving classification pro ...
Pro Serverless Data Handling with Microsoft Azure
Design and build architectures on the Microsoft Azure platform specifically for data-driven and ETL applications. Modern cloud architectures rely on serverless components more than ever, and this book helps you identify those components of data-driven or ETL applications that can be tackled using the technologies available on the Azure platform. The book shows you which Azure components are best suited to form a strong foundation for data-driven applications in the Microsoft Azure Cloud. If you are a solution architect or a decision maker, the conceptual aspects of this book will help you gain a deeper understanding of the underlying technology and its capabilities. You will understand how to develop using Azure Functions, Azure Data Factory, Logic Apps, and to employ serverless databases in your application to achieve the best scalability and design. If you are a developer, you will benefit from the hands-on approach used throughout this book. Many practical examples and architectu ...
Design and build architectures on the Microsoft Azure platform specifically for data-driven and ETL applications. Modern cloud architectures rely on serverless components more than ever, and this book helps you identify those components of data-driven or ETL applications that can be tackled using the technologies available on the Azure platform. The book shows you which Azure components are best suited to form a strong foundation for data-driven applications in the Microsoft Azure Cloud. If you are a solution architect or a decision maker, the conceptual aspects of this book will help you gain a deeper understanding of the underlying technology and its capabilities. You will understand how to develop using Azure Functions, Azure Data Factory, Logic Apps, and to employ serverless databases in your application to achieve the best scalability and design. If you are a developer, you will benefit from the hands-on approach used throughout this book. Many practical examples and architectu ...
Critical Data Literacy
A short course for students to increase their proficiency in analyzing and interpreting data visualizations. By completing this short course students will be able to explain the importance of data literacy, identify data visualization issues in order to improve their own skills in data story-telling. The intended outcome of this course is to help students become more discerning and critical users of data, graphs, charts and infographics. The need to understand data visualizations has never been more important. Every day we are inundated with more data, graphs and charts. Some of these data visualizations are well-designed and easy to understand, and others are confusing and misleading. Data literacy is often framed as a set of skills for data professionals, but we believe data literacy is for everyone. Everyone can benefit from improving their understanding of how data is created and their ability to analyze and interpret data. In this book, we will introduce the key stages in ...
A short course for students to increase their proficiency in analyzing and interpreting data visualizations. By completing this short course students will be able to explain the importance of data literacy, identify data visualization issues in order to improve their own skills in data story-telling. The intended outcome of this course is to help students become more discerning and critical users of data, graphs, charts and infographics. The need to understand data visualizations has never been more important. Every day we are inundated with more data, graphs and charts. Some of these data visualizations are well-designed and easy to understand, and others are confusing and misleading. Data literacy is often framed as a set of skills for data professionals, but we believe data literacy is for everyone. Everyone can benefit from improving their understanding of how data is created and their ability to analyze and interpret data. In this book, we will introduce the key stages in ...
Building an Effective Data Science Practice
Gain a deep understanding of data science and the thought process needed to solve problems in that field using the required techniques, technologies and skills that go into forming an interdisciplinary team. This book will enable you to set up an effective team of engineers, data scientists, analysts, and other stakeholders that can collaborate effectively on crucial aspects such as problem formulation, execution of experiments, and model performance evaluation. You'll start by delving into the fundamentals of data science - classes of data science problems, data science techniques and their applications - and gradually build up to building a professional reference operating model for a data science function in an organization. This operating model covers the roles and skills required in a team, the techniques and technologies they use, and the best practices typically followed in executing data science projects. Building an Effective Data Science Practice provides a common base ...
Gain a deep understanding of data science and the thought process needed to solve problems in that field using the required techniques, technologies and skills that go into forming an interdisciplinary team. This book will enable you to set up an effective team of engineers, data scientists, analysts, and other stakeholders that can collaborate effectively on crucial aspects such as problem formulation, execution of experiments, and model performance evaluation. You'll start by delving into the fundamentals of data science - classes of data science problems, data science techniques and their applications - and gradually build up to building a professional reference operating model for a data science function in an organization. This operating model covers the roles and skills required in a team, the techniques and technologies they use, and the best practices typically followed in executing data science projects. Building an Effective Data Science Practice provides a common base ...